The invention relates to a method for on-line handwriting recognition.
Human speech is presently being recognized by data processing methods that use hidden Markov processes and neural networks. Of late, similar algorithms are being used for online recognizing human handwriting during writing thereof. In the case of standard text, recognizing may imply correlating with various letter models, and assigning a particular letter sequence to the writing. In the case of a signature, recognizing may imply correlating with various different signatures, or rather verifying through correlating with just one signature. The modellings may be descriptions on some level of abstraction of the eventual text. The outputting may be restricted further with respect to the recognition, such as in the form an ASCII series, a stylized representation of the handwriting, the reproduction as spoken text, or just a yes/no output in the case of verification E. J. Bellegarda et al., A Fast Statistical Mixture Algorithm for On-line Handwriting Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(12) p.1227-1233, December 1994, describes letter recognition based on a splicing mechanism. K. S. Nathan et al., Real-time On-line Unconstrained Handwriting Recognition Using Statistical Methods, International Conference of Acoustics, Speech and Signal Processing, p.2619-2622, 1995, describes complete handwriting recognition based on a hidden Markov model (HMM).
Now, the present inventors have found that associating each handwriting interval with only a single feature vector, yields unsatisfactory recognition results. It is possible to derive each feature vector from all information present in a sequence of a plurality of handwriting intervals; this however leads to a sharply increased dimension of the feature vectors in terms of the number of components thereof, and requires unwieldy calculations, without yielding a corresponding improvement of the results. In consequence, there appears to be ample room for attaining improved effectivity of the present state of the art.